2D SKELETON-BASED HUMAN ACTION RECOGNITION USING ACTION-SNIPPET REPRESENTATION AND DEEP SEQUENTIAL NEURAL NETWORK

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dc.contributor.authorAskar, Aizada
dc.date.accessioned2022-06-06T09:28:37Z
dc.date.available2022-06-06T09:28:37Z
dc.date.issued2022-05
dc.description.abstractHuman action recognition is one of the crucial and important tasks in data science. It aims to understand human behavior and assign a label on performed action and has diverse applications. Domains, where this application is used, includes visual surveillance, human–computer interaction and video retrieval. Hence, discriminating human actions is a challenging problem with a lot of issues like motion performance, occlusions and dynamic background, and different data representations. There are many researches that explore various types of approaches for human action recognition. In this work we propose advanced geometric features and adequate deep sequential neural networks (DSNN) for 2D skeleton-based HAR. The 2D skeleton data used in this project are extracted from RGB video sequences, allowing the use of the proposed model to enrich contextual information. The 2D skeleton joint coordinates of the human are used to capture the spatial and temporal relationship between poses. We employ BiLSTM and Transformer models to classify human actions as they are capable of concurrently modeling spatial relationships between geometric characteristics of different body parts.en_US
dc.identifier.citationAskar, A. (2022). 2D skeleton-based Human Action Recognition using Action-Snippet Representation and Deep Sequential Neural Network (Unpublished master's thesis). Nazarbayev University, Nur-Sultan, Kazakhstanen_US
dc.identifier.urihttp://nur.nu.edu.kz/handle/123456789/6181
dc.language.isoenen_US
dc.publisherNazarbayev University School of Engineering and Digital Sciencesen_US
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectvisual surveillanceen_US
dc.subjectType of access: Open Accessen_US
dc.subjecthuman action recognitionen_US
dc.subjectdeep sequential neural networksen_US
dc.subjectDSNNen_US
dc.subject2D skeleton-based HARen_US
dc.subjectHARen_US
dc.subjectBiLSTMen_US
dc.subjectResearch Subject Categories::TECHNOLOGYen_US
dc.title2D SKELETON-BASED HUMAN ACTION RECOGNITION USING ACTION-SNIPPET REPRESENTATION AND DEEP SEQUENTIAL NEURAL NETWORKen_US
dc.typeMaster's thesisen_US
workflow.import.sourcescience

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